Overview

Dataset statistics

Number of variables21
Number of observations16880
Missing cells16061
Missing cells (%)4.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.7 MiB
Average record size in memory168.0 B

Variable types

Numeric18
Categorical3

Warnings

B is highly correlated with KHigh correlation
E is highly correlated with IHigh correlation
I is highly correlated with EHigh correlation
K is highly correlated with BHigh correlation
B is highly correlated with KHigh correlation
D is highly correlated with G and 1 other fieldsHigh correlation
E is highly correlated with F and 1 other fieldsHigh correlation
F is highly correlated with EHigh correlation
G is highly correlated with DHigh correlation
H is highly correlated with DHigh correlation
I is highly correlated with EHigh correlation
K is highly correlated with B and 1 other fieldsHigh correlation
S is highly correlated with KHigh correlation
B is highly correlated with KHigh correlation
D is highly correlated with G and 1 other fieldsHigh correlation
E is highly correlated with F and 1 other fieldsHigh correlation
F is highly correlated with EHigh correlation
G is highly correlated with DHigh correlation
H is highly correlated with DHigh correlation
I is highly correlated with EHigh correlation
K is highly correlated with BHigh correlation
K is highly correlated with S and 1 other fieldsHigh correlation
S is highly correlated with KHigh correlation
I is highly correlated with EHigh correlation
R is highly correlated with NHigh correlation
H is highly correlated with DHigh correlation
L is highly correlated with NHigh correlation
N is highly correlated with R and 1 other fieldsHigh correlation
B is highly correlated with KHigh correlation
D is highly correlated with HHigh correlation
E is highly correlated with IHigh correlation
C has 3197 (18.9%) missing values Missing
K has 12864 (76.2%) missing values Missing
D is highly skewed (γ1 = 49.39219407) Skewed
R is highly skewed (γ1 = 32.00153225) Skewed
A has 14234 (84.3%) zeros Zeros
D has 15935 (94.4%) zeros Zeros
E has 14257 (84.5%) zeros Zeros
F has 14925 (88.4%) zeros Zeros
G has 16425 (97.3%) zeros Zeros
H has 16597 (98.3%) zeros Zeros
I has 15998 (94.8%) zeros Zeros
L has 10847 (64.3%) zeros Zeros
Q has 16223 (96.1%) zeros Zeros
R has 16748 (99.2%) zeros Zeros

Reproduction

Analysis started2021-06-29 20:05:52.131395
Analysis finished2021-06-29 20:06:33.237922
Duration41.11 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

A
Real number (ℝ≥0)

ZEROS

Distinct21
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3092417062
Minimum0
Maximum30
Zeros14234
Zeros (%)84.3%
Negative0
Negative (%)0.0%
Memory size132.0 KiB
2021-06-29T15:06:33.318649image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum30
Range30
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.094063994
Coefficient of variation (CV)3.537892762
Kurtosis151.9499537
Mean0.3092417062
Median Absolute Deviation (MAD)0
Skewness9.258850455
Sum5220
Variance1.196976023
MonotonicityNot monotonic
2021-06-29T15:06:33.429165image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
014234
84.3%
11554
 
9.2%
2564
 
3.3%
3233
 
1.4%
4125
 
0.7%
551
 
0.3%
643
 
0.3%
721
 
0.1%
818
 
0.1%
106
 
< 0.1%
Other values (11)31
 
0.2%
ValueCountFrequency (%)
014234
84.3%
11554
 
9.2%
2564
 
3.3%
3233
 
1.4%
4125
 
0.7%
551
 
0.3%
643
 
0.3%
721
 
0.1%
818
 
0.1%
95
 
< 0.1%
ValueCountFrequency (%)
301
 
< 0.1%
273
< 0.1%
203
< 0.1%
191
 
< 0.1%
172
 
< 0.1%
151
 
< 0.1%
145
< 0.1%
134
< 0.1%
123
< 0.1%
113
< 0.1%

B
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct21
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.645379147
Minimum-1
Maximum20
Zeros0
Zeros (%)0.0%
Negative125
Negative (%)0.7%
Memory size132.0 KiB
2021-06-29T15:06:33.548166image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile1
Q14
median7
Q311
95-th percentile16
Maximum20
Range21
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.68649367
Coefficient of variation (CV)0.6129838141
Kurtosis-0.7221092532
Mean7.645379147
Median Absolute Deviation (MAD)4
Skewness0.3718930065
Sum129054
Variance21.96322291
MonotonicityNot monotonic
2021-06-29T15:06:33.701163image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
11342
 
8.0%
31261
 
7.5%
21241
 
7.4%
41230
 
7.3%
71212
 
7.2%
51193
 
7.1%
61170
 
6.9%
81154
 
6.8%
91139
 
6.7%
101089
 
6.5%
Other values (11)4849
28.7%
ValueCountFrequency (%)
-1125
 
0.7%
11342
8.0%
21241
7.4%
31261
7.5%
41230
7.3%
51193
7.1%
61170
6.9%
71212
7.2%
81154
6.8%
91139
6.7%
ValueCountFrequency (%)
2049
 
0.3%
19112
 
0.7%
18194
 
1.1%
17254
 
1.5%
16489
2.9%
15503
3.0%
14613
3.6%
13726
4.3%
12879
5.2%
11905
5.4%

C
Real number (ℝ≥0)

MISSING

Distinct9782
Distinct (%)71.5%
Missing3197
Missing (%)18.9%
Infinite0
Infinite (%)0.0%
Mean39235.33998
Minimum0
Maximum617324
Zeros143
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size132.0 KiB
2021-06-29T15:06:33.855166image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile34
Q11172
median6173
Q326888.5
95-th percentile324839
Maximum617324
Range617324
Interquartile range (IQR)25716.5

Descriptive statistics

Standard deviation91045.25288
Coefficient of variation (CV)2.320490989
Kurtosis12.21021267
Mean39235.33998
Median Absolute Deviation (MAD)6001
Skewness3.502602667
Sum536857157
Variance8289238072
MonotonicityNot monotonic
2021-06-29T15:06:34.016781image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0143
 
0.8%
437
 
0.2%
235
 
0.2%
632
 
0.2%
329
 
0.2%
826
 
0.2%
1825
 
0.1%
924
 
0.1%
1023
 
0.1%
1419
 
0.1%
Other values (9772)13290
78.7%
(Missing)3197
 
18.9%
ValueCountFrequency (%)
0143
0.8%
14
 
< 0.1%
235
 
0.2%
329
 
0.2%
437
 
0.2%
511
 
0.1%
632
 
0.2%
78
 
< 0.1%
826
 
0.2%
924
 
0.1%
ValueCountFrequency (%)
6173241
< 0.1%
6133271
< 0.1%
5918491
< 0.1%
5896341
< 0.1%
5710621
< 0.1%
5707791
< 0.1%
5700791
< 0.1%
5694821
< 0.1%
5693711
< 0.1%
5659851
< 0.1%

D
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct31
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1986966825
Minimum0
Maximum180
Zeros15935
Zeros (%)94.4%
Negative0
Negative (%)0.0%
Memory size132.0 KiB
2021-06-29T15:06:34.161782image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum180
Range180
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.042959025
Coefficient of variation (CV)10.28179736
Kurtosis3790.144004
Mean0.1986966825
Median Absolute Deviation (MAD)0
Skewness49.39219407
Sum3354
Variance4.173681576
MonotonicityNot monotonic
2021-06-29T15:06:34.287048image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
015935
94.4%
1415
 
2.5%
2176
 
1.0%
3115
 
0.7%
497
 
0.6%
632
 
0.2%
822
 
0.1%
516
 
0.1%
713
 
0.1%
1213
 
0.1%
Other values (21)46
 
0.3%
ValueCountFrequency (%)
015935
94.4%
1415
 
2.5%
2176
 
1.0%
3115
 
0.7%
497
 
0.6%
516
 
0.1%
632
 
0.2%
713
 
0.1%
822
 
0.1%
93
 
< 0.1%
ValueCountFrequency (%)
1801
 
< 0.1%
801
 
< 0.1%
601
 
< 0.1%
421
 
< 0.1%
402
< 0.1%
361
 
< 0.1%
331
 
< 0.1%
301
 
< 0.1%
271
 
< 0.1%
243
< 0.1%

E
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct23
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4337085308
Minimum0
Maximum45
Zeros14257
Zeros (%)84.5%
Negative0
Negative (%)0.0%
Memory size132.0 KiB
2021-06-29T15:06:34.411583image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3
Maximum45
Range45
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.53729665
Coefficient of variation (CV)3.54453865
Kurtosis109.7704487
Mean0.4337085308
Median Absolute Deviation (MAD)0
Skewness7.758762443
Sum7321
Variance2.363280991
MonotonicityNot monotonic
2021-06-29T15:06:34.520549image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
014257
84.5%
11163
 
6.9%
2466
 
2.8%
3325
 
1.9%
4300
 
1.8%
699
 
0.6%
880
 
0.5%
569
 
0.4%
1022
 
0.1%
1222
 
0.1%
Other values (13)77
 
0.5%
ValueCountFrequency (%)
014257
84.5%
11163
 
6.9%
2466
 
2.8%
3325
 
1.9%
4300
 
1.8%
569
 
0.4%
699
 
0.6%
721
 
0.1%
880
 
0.5%
921
 
0.1%
ValueCountFrequency (%)
451
 
< 0.1%
351
 
< 0.1%
301
 
< 0.1%
271
 
< 0.1%
251
 
< 0.1%
243
< 0.1%
187
< 0.1%
167
< 0.1%
154
< 0.1%
147
< 0.1%

F
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct153
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.01587849526
Minimum0
Maximum1
Zeros14925
Zeros (%)88.4%
Negative0
Negative (%)0.0%
Memory size132.0 KiB
2021-06-29T15:06:34.652131image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.038
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.09873035094
Coefficient of variation (CV)6.217865693
Kurtosis77.51231699
Mean0.01587849526
Median Absolute Deviation (MAD)0
Skewness8.524194942
Sum268.029
Variance0.009747682196
MonotonicityNot monotonic
2021-06-29T15:06:34.806098image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
014925
88.4%
1130
 
0.8%
0.00296
 
0.6%
0.00386
 
0.5%
0.00472
 
0.4%
0.00666
 
0.4%
0.00562
 
0.4%
0.00152
 
0.3%
0.2546
 
0.3%
0.0146
 
0.3%
Other values (143)1299
 
7.7%
ValueCountFrequency (%)
014925
88.4%
0.00152
 
0.3%
0.00296
 
0.6%
0.00386
 
0.5%
0.00472
 
0.4%
0.00562
 
0.4%
0.00666
 
0.4%
0.00746
 
0.3%
0.00844
 
0.3%
0.00927
 
0.2%
ValueCountFrequency (%)
1130
0.8%
0.66719
 
0.1%
0.541
 
0.2%
0.4581
 
< 0.1%
0.4293
 
< 0.1%
0.41718
 
0.1%
0.3757
 
< 0.1%
0.353
 
< 0.1%
0.33343
 
0.3%
0.3061
 
< 0.1%

G
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct106
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.005200296209
Minimum0
Maximum1
Zeros16425
Zeros (%)97.3%
Negative0
Negative (%)0.0%
Memory size132.0 KiB
2021-06-29T15:06:34.960099image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.05941924428
Coefficient of variation (CV)11.42612688
Kurtosis235.4130992
Mean0.005200296209
Median Absolute Deviation (MAD)0
Skewness14.86459402
Sum87.781
Variance0.00353064659
MonotonicityNot monotonic
2021-06-29T15:06:35.107242image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
016425
97.3%
149
 
0.3%
0.2525
 
0.1%
0.00316
 
0.1%
0.02212
 
0.1%
0.01312
 
0.1%
0.00112
 
0.1%
0.00211
 
0.1%
0.511
 
0.1%
0.00911
 
0.1%
Other values (96)296
 
1.8%
ValueCountFrequency (%)
016425
97.3%
0.00112
 
0.1%
0.00211
 
0.1%
0.00316
 
0.1%
0.0047
 
< 0.1%
0.0058
 
< 0.1%
0.00610
 
0.1%
0.0076
 
< 0.1%
0.0086
 
< 0.1%
0.00911
 
0.1%
ValueCountFrequency (%)
149
0.3%
0.6675
 
< 0.1%
0.6111
 
< 0.1%
0.511
 
0.1%
0.4174
 
< 0.1%
0.3941
 
< 0.1%
0.3338
 
< 0.1%
0.3072
 
< 0.1%
0.281
 
< 0.1%
0.2781
 
< 0.1%

H
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct14
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.05029620853
Minimum0
Maximum21
Zeros16597
Zeros (%)98.3%
Negative0
Negative (%)0.0%
Memory size132.0 KiB
2021-06-29T15:06:35.230211image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum21
Range21
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.531060179
Coefficient of variation (CV)10.55865232
Kurtosis452.5079917
Mean0.05029620853
Median Absolute Deviation (MAD)0
Skewness18.06245947
Sum849
Variance0.2820249137
MonotonicityNot monotonic
2021-06-29T15:06:35.325829image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
016597
98.3%
1100
 
0.6%
259
 
0.3%
343
 
0.3%
441
 
0.2%
614
 
0.1%
89
 
0.1%
54
 
< 0.1%
124
 
< 0.1%
73
 
< 0.1%
Other values (4)6
 
< 0.1%
ValueCountFrequency (%)
016597
98.3%
1100
 
0.6%
259
 
0.3%
343
 
0.3%
441
 
0.2%
54
 
< 0.1%
614
 
0.1%
73
 
< 0.1%
89
 
0.1%
91
 
< 0.1%
ValueCountFrequency (%)
211
 
< 0.1%
181
 
< 0.1%
153
 
< 0.1%
124
 
< 0.1%
91
 
< 0.1%
89
 
0.1%
73
 
< 0.1%
614
 
0.1%
54
 
< 0.1%
441
0.2%

I
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct17
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1440758294
Minimum0
Maximum24
Zeros15998
Zeros (%)94.8%
Negative0
Negative (%)0.0%
Memory size132.0 KiB
2021-06-29T15:06:35.419995image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum24
Range24
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.8247607657
Coefficient of variation (CV)5.724490841
Kurtosis128.3258611
Mean0.1440758294
Median Absolute Deviation (MAD)0
Skewness9.356207256
Sum2432
Variance0.6802303207
MonotonicityNot monotonic
2021-06-29T15:06:35.525967image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
015998
94.8%
1360
 
2.1%
2150
 
0.9%
3140
 
0.8%
4111
 
0.7%
642
 
0.2%
523
 
0.1%
818
 
0.1%
711
 
0.1%
109
 
0.1%
Other values (7)18
 
0.1%
ValueCountFrequency (%)
015998
94.8%
1360
 
2.1%
2150
 
0.9%
3140
 
0.8%
4111
 
0.7%
523
 
0.1%
642
 
0.2%
711
 
0.1%
818
 
0.1%
94
 
< 0.1%
ValueCountFrequency (%)
241
 
< 0.1%
162
 
< 0.1%
151
 
< 0.1%
142
 
< 0.1%
127
 
< 0.1%
111
 
< 0.1%
109
0.1%
94
 
< 0.1%
818
0.1%
711
0.1%

J
Categorical

Distinct19
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size132.0 KiB
AR
9329 
BR
4428 
MX
2366 
ES
 
314
US
 
230
Other values (14)
 
213

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters33760
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)0.1%

Sample

1st rowUY
2nd rowUY
3rd rowUY
4th rowUY
5th rowUY

Common Values

ValueCountFrequency (%)
AR9329
55.3%
BR4428
26.2%
MX2366
 
14.0%
ES314
 
1.9%
US230
 
1.4%
UY180
 
1.1%
CA12
 
0.1%
GB8
 
< 0.1%
FR2
 
< 0.1%
GT2
 
< 0.1%
Other values (9)9
 
0.1%

Length

2021-06-29T15:06:35.748102image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ar9329
55.3%
br4428
26.2%
mx2366
 
14.0%
es314
 
1.9%
us230
 
1.4%
uy180
 
1.1%
ca12
 
0.1%
gb8
 
< 0.1%
fr2
 
< 0.1%
gt2
 
< 0.1%
Other values (9)9
 
0.1%

Most occurring characters

ValueCountFrequency (%)
R13761
40.8%
A9343
27.7%
B4436
 
13.1%
M2366
 
7.0%
X2366
 
7.0%
S544
 
1.6%
U412
 
1.2%
E314
 
0.9%
Y180
 
0.5%
C15
 
< 0.1%
Other values (9)23
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter33760
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R13761
40.8%
A9343
27.7%
B4436
 
13.1%
M2366
 
7.0%
X2366
 
7.0%
S544
 
1.6%
U412
 
1.2%
E314
 
0.9%
Y180
 
0.5%
C15
 
< 0.1%
Other values (9)23
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin33760
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
R13761
40.8%
A9343
27.7%
B4436
 
13.1%
M2366
 
7.0%
X2366
 
7.0%
S544
 
1.6%
U412
 
1.2%
E314
 
0.9%
Y180
 
0.5%
C15
 
< 0.1%
Other values (9)23
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII33760
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R13761
40.8%
A9343
27.7%
B4436
 
13.1%
M2366
 
7.0%
X2366
 
7.0%
S544
 
1.6%
U412
 
1.2%
E314
 
0.9%
Y180
 
0.5%
C15
 
< 0.1%
Other values (9)23
 
0.1%

K
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct86
Distinct (%)2.1%
Missing12864
Missing (%)76.2%
Infinite0
Infinite (%)0.0%
Mean0.6820991036
Minimum0.12
Maximum0.99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size132.0 KiB
2021-06-29T15:06:35.873065image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0.12
5-th percentile0.43
Q10.58
median0.68
Q30.8
95-th percentile0.93
Maximum0.99
Range0.87
Interquartile range (IQR)0.22

Descriptive statistics

Standard deviation0.1532638716
Coefficient of variation (CV)0.2246944334
Kurtosis-0.2041829345
Mean0.6820991036
Median Absolute Deviation (MAD)0.11
Skewness-0.2434129735
Sum2739.31
Variance0.02348981434
MonotonicityNot monotonic
2021-06-29T15:06:36.014067image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.67113
 
0.7%
0.58113
 
0.7%
0.62111
 
0.7%
0.74106
 
0.6%
0.71106
 
0.6%
0.56100
 
0.6%
0.7396
 
0.6%
0.695
 
0.6%
0.6393
 
0.6%
0.7591
 
0.5%
Other values (76)2992
 
17.7%
(Missing)12864
76.2%
ValueCountFrequency (%)
0.121
 
< 0.1%
0.142
< 0.1%
0.151
 
< 0.1%
0.171
 
< 0.1%
0.182
< 0.1%
0.193
< 0.1%
0.23
< 0.1%
0.212
< 0.1%
0.222
< 0.1%
0.232
< 0.1%
ValueCountFrequency (%)
0.996
 
< 0.1%
0.9833
0.2%
0.9720
 
0.1%
0.9643
0.3%
0.9537
0.2%
0.9437
0.2%
0.9342
0.2%
0.9251
0.3%
0.9137
0.2%
0.942
0.2%

L
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4323459716
Minimum0
Maximum7
Zeros10847
Zeros (%)64.3%
Negative0
Negative (%)0.0%
Memory size132.0 KiB
2021-06-29T15:06:36.448762image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum7
Range7
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.6638597023
Coefficient of variation (CV)1.535482567
Kurtosis5.635365176
Mean0.4323459716
Median Absolute Deviation (MAD)0
Skewness1.876080003
Sum7298
Variance0.4407097043
MonotonicityNot monotonic
2021-06-29T15:06:36.523678image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
010847
64.3%
15058
30.0%
2748
 
4.4%
3186
 
1.1%
426
 
0.2%
59
 
0.1%
65
 
< 0.1%
71
 
< 0.1%
ValueCountFrequency (%)
010847
64.3%
15058
30.0%
2748
 
4.4%
3186
 
1.1%
426
 
0.2%
59
 
0.1%
65
 
< 0.1%
71
 
< 0.1%
ValueCountFrequency (%)
71
 
< 0.1%
65
 
< 0.1%
59
 
0.1%
426
 
0.2%
3186
 
1.1%
2748
 
4.4%
15058
30.0%
010847
64.3%

M
Real number (ℝ≥0)

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.544135071
Minimum1
Maximum13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size132.0 KiB
2021-06-29T15:06:36.600575image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile3
Maximum13
Range12
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.022384672
Coefficient of variation (CV)0.6621083162
Kurtosis12.79569976
Mean1.544135071
Median Absolute Deviation (MAD)0
Skewness2.972555044
Sum26065
Variance1.045270417
MonotonicityNot monotonic
2021-06-29T15:06:36.689071image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
111344
67.2%
23448
 
20.4%
31308
 
7.7%
4386
 
2.3%
5202
 
1.2%
690
 
0.5%
752
 
0.3%
820
 
0.1%
920
 
0.1%
109
 
0.1%
ValueCountFrequency (%)
111344
67.2%
23448
 
20.4%
31308
 
7.7%
4386
 
2.3%
5202
 
1.2%
690
 
0.5%
752
 
0.3%
820
 
0.1%
920
 
0.1%
109
 
0.1%
ValueCountFrequency (%)
131
 
< 0.1%
109
 
0.1%
920
 
0.1%
820
 
0.1%
752
 
0.3%
690
 
0.5%
5202
 
1.2%
4386
 
2.3%
31308
 
7.7%
23448
20.4%

N
Real number (ℝ≥0)

HIGH CORRELATION

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.091765403
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size132.0 KiB
2021-06-29T15:06:36.780112image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum10
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.4080805492
Coefficient of variation (CV)0.3737804368
Kurtosis70.83407697
Mean1.091765403
Median Absolute Deviation (MAD)0
Skewness6.842797754
Sum18429
Variance0.1665297346
MonotonicityNot monotonic
2021-06-29T15:06:36.873072image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
115767
93.4%
2826
 
4.9%
3201
 
1.2%
451
 
0.3%
522
 
0.1%
65
 
< 0.1%
74
 
< 0.1%
82
 
< 0.1%
91
 
< 0.1%
101
 
< 0.1%
ValueCountFrequency (%)
115767
93.4%
2826
 
4.9%
3201
 
1.2%
451
 
0.3%
522
 
0.1%
65
 
< 0.1%
74
 
< 0.1%
82
 
< 0.1%
91
 
< 0.1%
101
 
< 0.1%
ValueCountFrequency (%)
101
 
< 0.1%
91
 
< 0.1%
82
 
< 0.1%
74
 
< 0.1%
65
 
< 0.1%
522
 
0.1%
451
 
0.3%
3201
 
1.2%
2826
 
4.9%
115767
93.4%

O
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size132.0 KiB
0
16750 
1
 
108
2
 
15
3
 
7

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters16880
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
016750
99.2%
1108
 
0.6%
215
 
0.1%
37
 
< 0.1%

Length

2021-06-29T15:06:37.084654image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-29T15:06:37.160655image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
016750
99.2%
1108
 
0.6%
215
 
0.1%
37
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
016750
99.2%
1108
 
0.6%
215
 
0.1%
37
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number16880
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
016750
99.2%
1108
 
0.6%
215
 
0.1%
37
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common16880
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
016750
99.2%
1108
 
0.6%
215
 
0.1%
37
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII16880
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
016750
99.2%
1108
 
0.6%
215
 
0.1%
37
 
< 0.1%

P
Real number (ℝ≥0)

Distinct14
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.630864929
Minimum1
Maximum41
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size132.0 KiB
2021-06-29T15:06:37.239655image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile4
Maximum41
Range40
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.088285962
Coefficient of variation (CV)0.667306006
Kurtosis108.2574792
Mean1.630864929
Median Absolute Deviation (MAD)0
Skewness4.776851493
Sum27529
Variance1.184366335
MonotonicityNot monotonic
2021-06-29T15:06:37.342798image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
110787
63.9%
23383
 
20.0%
31464
 
8.7%
4814
 
4.8%
5376
 
2.2%
630
 
0.2%
78
 
< 0.1%
88
 
< 0.1%
134
 
< 0.1%
102
 
< 0.1%
Other values (4)4
 
< 0.1%
ValueCountFrequency (%)
110787
63.9%
23383
 
20.0%
31464
 
8.7%
4814
 
4.8%
5376
 
2.2%
630
 
0.2%
78
 
< 0.1%
88
 
< 0.1%
91
 
< 0.1%
102
 
< 0.1%
ValueCountFrequency (%)
411
 
< 0.1%
151
 
< 0.1%
134
 
< 0.1%
111
 
< 0.1%
102
 
< 0.1%
91
 
< 0.1%
88
 
< 0.1%
78
 
< 0.1%
630
 
0.2%
5376
2.2%

Q
Real number (ℝ≥0)

ZEROS

Distinct605
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.445393957
Minimum0
Maximum2274.67
Zeros16223
Zeros (%)96.1%
Negative0
Negative (%)0.0%
Memory size132.0 KiB
2021-06-29T15:06:37.462783image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum2274.67
Range2274.67
Interquartile range (IQR)0

Descriptive statistics

Standard deviation74.36772853
Coefficient of variation (CV)8.805714559
Kurtosis356.8723368
Mean8.445393957
Median Absolute Deviation (MAD)0
Skewness16.49927377
Sum142558.25
Variance5530.559047
MonotonicityNot monotonic
2021-06-29T15:06:37.591753image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
016223
96.1%
542.765
 
< 0.1%
159.034
 
< 0.1%
1912.023
 
< 0.1%
30.693
 
< 0.1%
31.752
 
< 0.1%
505.82
 
< 0.1%
437.172
 
< 0.1%
502
 
< 0.1%
30.312
 
< 0.1%
Other values (595)632
 
3.7%
ValueCountFrequency (%)
016223
96.1%
1.592
 
< 0.1%
3.721
 
< 0.1%
5.31
 
< 0.1%
6.361
 
< 0.1%
7.841
 
< 0.1%
7.911
 
< 0.1%
10.011
 
< 0.1%
10.551
 
< 0.1%
10.621
 
< 0.1%
ValueCountFrequency (%)
2274.671
 
< 0.1%
2263.431
 
< 0.1%
2082.041
 
< 0.1%
1912.023
< 0.1%
1884.881
 
< 0.1%
1857.741
 
< 0.1%
1697.931
 
< 0.1%
1538.741
 
< 0.1%
1379.551
 
< 0.1%
1345.081
 
< 0.1%

R
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct109
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.994921209
Minimum0
Maximum2025.72
Zeros16748
Zeros (%)99.2%
Negative0
Negative (%)0.0%
Memory size132.0 KiB
2021-06-29T15:06:37.718325image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum2025.72
Range2025.72
Interquartile range (IQR)0

Descriptive statistics

Standard deviation38.22248507
Coefficient of variation (CV)19.15989709
Kurtosis1299.178947
Mean1.994921209
Median Absolute Deviation (MAD)0
Skewness32.00153225
Sum33674.27
Variance1460.958365
MonotonicityNot monotonic
2021-06-29T15:06:37.853296image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
016748
99.2%
52.165
 
< 0.1%
559.214
 
< 0.1%
194.813
 
< 0.1%
13.782
 
< 0.1%
39.782
 
< 0.1%
687.532
 
< 0.1%
2025.722
 
< 0.1%
294.722
 
< 0.1%
30.762
 
< 0.1%
Other values (99)108
 
0.6%
ValueCountFrequency (%)
016748
99.2%
13.782
 
< 0.1%
14.191
 
< 0.1%
15.081
 
< 0.1%
17.921
 
< 0.1%
18.021
 
< 0.1%
19.011
 
< 0.1%
21.151
 
< 0.1%
21.251
 
< 0.1%
22.161
 
< 0.1%
ValueCountFrequency (%)
2025.722
< 0.1%
1431.181
< 0.1%
1112.431
< 0.1%
1055.461
< 0.1%
1033.551
< 0.1%
984.441
< 0.1%
919.841
< 0.1%
912.462
< 0.1%
818.121
< 0.1%
766.841
< 0.1%

S
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION

Distinct6467
Distinct (%)38.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.12772038
Minimum-1
Maximum99.97
Zeros0
Zeros (%)0.0%
Negative136
Negative (%)0.8%
Memory size132.0 KiB
2021-06-29T15:06:37.995293image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile1.5
Q19.56
median20.64
Q339.2125
95-th percentile89.7705
Maximum99.97
Range100.97
Interquartile range (IQR)29.6525

Descriptive statistics

Standard deviation26.51054805
Coefficient of variation (CV)0.9101483984
Kurtosis0.489546419
Mean29.12772038
Median Absolute Deviation (MAD)13.13
Skewness1.213007308
Sum491675.92
Variance702.809158
MonotonicityNot monotonic
2021-06-29T15:06:38.126846image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1136
 
0.8%
7.2512
 
0.1%
3.8812
 
0.1%
0.5312
 
0.1%
6.5111
 
0.1%
1.1711
 
0.1%
11.6111
 
0.1%
0.5511
 
0.1%
15.4710
 
0.1%
6.5310
 
0.1%
Other values (6457)16644
98.6%
ValueCountFrequency (%)
-1136
0.8%
0.071
 
< 0.1%
0.081
 
< 0.1%
0.092
 
< 0.1%
0.15
 
< 0.1%
0.112
 
< 0.1%
0.122
 
< 0.1%
0.134
 
< 0.1%
0.143
 
< 0.1%
0.151
 
< 0.1%
ValueCountFrequency (%)
99.971
< 0.1%
99.921
< 0.1%
99.911
< 0.1%
99.821
< 0.1%
99.811
< 0.1%
99.771
< 0.1%
99.711
< 0.1%
99.691
< 0.1%
99.681
< 0.1%
99.641
< 0.1%

Monto
Real number (ℝ≥0)

Distinct9616
Distinct (%)57.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean161.8370332
Minimum0.05
Maximum12538.44
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size132.0 KiB
2021-06-29T15:06:38.255883image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0.05
5-th percentile14.59
Q133.8075
median81.645
Q3193.44
95-th percentile531.9285
Maximum12538.44
Range12538.39
Interquartile range (IQR)159.6325

Descriptive statistics

Standard deviation275.4999614
Coefficient of variation (CV)1.702329535
Kurtosis331.6162788
Mean161.8370332
Median Absolute Deviation (MAD)55.795
Skewness11.72324957
Sum2731809.12
Variance75900.22874
MonotonicityNot monotonic
2021-06-29T15:06:38.393280image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26.4549
 
0.3%
29.143
 
0.3%
31.7542
 
0.2%
37.0537
 
0.2%
29.1633
 
0.2%
26.5829
 
0.2%
158.9829
 
0.2%
29.2428
 
0.2%
21.1527
 
0.2%
10.5526
 
0.2%
Other values (9606)16537
98.0%
ValueCountFrequency (%)
0.051
 
< 0.1%
0.272
< 0.1%
0.371
 
< 0.1%
0.481
 
< 0.1%
0.534
< 0.1%
0.581
 
< 0.1%
0.641
 
< 0.1%
0.81
 
< 0.1%
0.911
 
< 0.1%
0.961
 
< 0.1%
ValueCountFrequency (%)
12538.441
< 0.1%
7089.761
< 0.1%
5831.162
< 0.1%
5332.671
< 0.1%
5317.431
< 0.1%
5306.951
< 0.1%
4130.31
< 0.1%
3975.791
< 0.1%
3830.091
< 0.1%
3530.381
< 0.1%

Fraude
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size132.0 KiB
0
12269 
1
4611 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters16880
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
012269
72.7%
14611
 
27.3%

Length

2021-06-29T15:06:38.603279image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-29T15:06:38.665823image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
012269
72.7%
14611
 
27.3%

Most occurring characters

ValueCountFrequency (%)
012269
72.7%
14611
 
27.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number16880
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
012269
72.7%
14611
 
27.3%

Most occurring scripts

ValueCountFrequency (%)
Common16880
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
012269
72.7%
14611
 
27.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII16880
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
012269
72.7%
14611
 
27.3%

Interactions

2021-06-29T15:05:56.533070image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:05:56.634433image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:05:56.723399image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:05:56.817433image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:05:56.910433image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:05:57.002397image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:05:57.098041image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:05:57.192077image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:05:57.279077image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:05:57.366052image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:05:57.454046image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:05:57.545050image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:05:57.634884image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:05:57.722007image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:05:57.817970image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:05:57.908969image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:05:58.007968image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:05:58.190507image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:05:58.290967image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:05:58.384966image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:05:58.474929image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:05:58.578933image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:05:58.673163image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:05:58.760163image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:05:58.850234image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:05:58.943236image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:05:59.030235image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:05:59.119313image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:05:59.226314image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:05:59.333314image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:05:59.434314image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:05:59.529313image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:05:59.633312image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:05:59.727311image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:05:59.831313image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:05:59.935506image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:00.036159image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:00.142922image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:00.244891image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:00.358891image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:00.476924image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:00.583105image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:00.691010image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:00.796905image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:00.921940image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:01.038120image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:01.140728image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:01.250729image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:01.357746image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:01.467666image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:01.700541image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:01.804540image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:01.917507image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:02.023505image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:02.133547image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:02.239544image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:02.341068image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:02.451068image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:02.565066image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:02.677149image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:02.790140image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:02.898139image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:03.002113image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:03.108113image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:03.216251image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:03.324258image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:03.428258image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:03.528254image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:03.642254image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:03.752837image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:03.872809image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:03.984857image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:04.099849image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:04.199004image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:04.294004image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:04.393003image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:04.492038image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:04.586036image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:04.684550image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:04.785589image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:04.886581image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:04.986554image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:05.098550image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:05.204633image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:05.303112image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:05.399114image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:05.503246image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:05.598375image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:05.702056image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:05.801911image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:06.046911image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:06.153909image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:06.252995image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:06.358640image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:06.467518image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:06.573011image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:06.679203image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:06.784765image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:06.887837image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:07.000728image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:07.113633image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:07.225600image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:07.332077image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:07.434114image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:07.541108image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:07.641112image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:07.748824image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:07.846821image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:07.950790image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:08.057828image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:08.165789image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:08.278973image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:08.391946image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:08.491949image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:08.595949image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:08.695946image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:08.798721image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:08.895760image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:08.992752image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:09.102894image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:09.207856image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:09.312970image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:09.425004image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:09.525970image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:09.635006image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:09.734011image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:09.835189image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:09.932284image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:10.024286image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:10.137253image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:10.241269image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:10.340851image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:10.446851image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:10.547453image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:10.641974image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:10.736975image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:10.838162image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:10.936309image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:11.029943image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:11.122944image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:11.393115image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:11.492116image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:11.602116image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:11.700115image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:11.799667image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:11.899667image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:11.994666image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:12.105310image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:12.209563image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:12.306117image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:12.408082image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:12.513088image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:12.617114image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:12.718112image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:12.820775image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:12.923769image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:13.019742image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:13.116742image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:13.221660image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:13.318880image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:13.425881image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:13.528880image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:13.635881image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:13.739909image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:13.844548image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:13.942122image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:14.045127image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:14.148759image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:14.251795image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:14.359895image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:14.468932image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:14.576894image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:14.680934image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:14.788895image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:14.894846image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:15.000851image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:15.098890image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:15.193893image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:15.295300image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:15.388966image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:15.488134image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:15.593107image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:15.696107image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:15.809108image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:15.924214image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:16.031252image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:16.139250image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:16.245247image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:16.349773image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:16.452794image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:16.554938image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:16.661937image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:16.764939image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:16.870564image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:16.987526image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:17.096557image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:17.216523image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:17.326561image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:17.435795image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:17.535783image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:17.832959image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:17.932604image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:18.035614image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:18.135610image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:18.237607image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:18.338158image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:18.434952image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:18.529984image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:18.625985image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:18.725984image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:18.819635image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:18.913274image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:19.021317image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:19.124320image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:19.238313image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:19.343278image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:19.450294image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:19.552088image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:19.647088image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:19.746086image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:19.844129image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:19.936263image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:20.031649image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:20.132432image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:20.230432image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:20.330809image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:20.441512image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:20.545039image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:20.641052image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:20.736054image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:20.837054image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:20.931097image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:21.034253image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:21.130221image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:21.231221image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:21.345215image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:21.459300image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:21.577299image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:21.694300image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:21.801299image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:21.909908image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:22.017908image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:22.123054image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:22.227052image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:22.327292image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:22.457872image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:22.577905image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:22.694872image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:22.816871image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:22.926950image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:23.057981image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:23.178552image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:23.297614image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:23.400253image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:23.502904image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:23.612867image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:23.722047image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:23.820994image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:23.921148image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:24.020142image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:24.115192image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:24.213181image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:24.318031image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:24.426707image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:24.535300image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:24.641300image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:24.757332image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:24.863301image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:24.976735image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:25.088733image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:25.199854image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:25.324730image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:25.439396image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:25.565055image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:25.947138image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:26.061139image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:26.176169image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:26.289175image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:26.398648image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:26.506285image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:26.613252image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:26.733254image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:26.854256image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:26.975341image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:27.104310image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:27.222345image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:27.352103image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:27.473074image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:27.598978image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:27.709981image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:27.811978image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:27.921979image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:28.038046image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:28.147124image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:28.309261image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:28.455265image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:28.600639image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:28.736720image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:28.867687image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:29.014251image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:29.145703image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:29.284661image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:29.591369image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:29.780401image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:29.944967image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:30.077980image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:30.209981image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:30.340549image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:30.460546image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:30.584844image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:30.708875image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:30.821877image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:30.933472image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:31.042497image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:31.151009image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:31.264014image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:31.384051image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:31.509050image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:31.627632image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:31.742632image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:31.861632image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:31.965631image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:32.081685image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-29T15:06:32.190698image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Correlations

2021-06-29T15:06:38.753387image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-06-29T15:06:38.958965image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-06-29T15:06:39.167699image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-06-29T15:06:39.378928image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-06-29T15:06:39.545928image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-06-29T15:06:32.446221image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-06-29T15:06:32.826772image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-06-29T15:06:33.024728image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-06-29T15:06:33.127955image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

ABCDEFGHIJKLMNOPQRSMontoFraude
001050257.00000.000.0000UY0.80031050.000.007.2537.511
101029014.00000.000.0000UYNaN011030.000.0011.668.181
20792.00010.000.0001UYNaN031020.000.0086.9713.961
391650269.00000.000.0000UY0.91031050.000.002.5193.671
4088180.00000.000.0000UYNaN011010.000.0025.96135.401
51121141.00000.000.0000UY0.47011010.000.0031.6431.211
610184560.00000.000.0000UY0.67031050.000.002.5526.991
701038.00000.000.0000UYNaN011150.000.0026.7283.301
80143790.00000.000.0000UY0.56011150.000.0043.2130.891
901623210.00000.000.0000UYNaN111020.000.009.2752.051

Last rows

ABCDEFGHIJKLMNOPQRSMontoFraude
1687001315153.00000.000.0000BR0.71021030.000.0088.8661.881
1687121519937.00000.000.0000BRNaN021010.000.0065.25345.961
1687211388579.00700.000.0000BRNaN111010.000.0040.94316.741
168731189008.00000.000.0000BRNaN011010.000.0030.14521.581
1687404468467.00000.000.0000BRNaN011010.000.0016.15128.411
168750363302.00010.500.0000BRNaN111010.000.0085.73132.171
16876012825.00000.000.0000BRNaN111050.000.0012.5026.341
168771381067.00000.000.0000BR0.72011010.000.0024.16195.631
1687809398372.00000.000.0000BRNaN021010.000.0029.0036.971
1687901215128.00030.000.0000BRNaN021010.000.0087.0821.581